Summary: As businesses create more data than ever before, Business Intelligence is growing. The only problem: Most Business Intelligence projects still fail. In this article, you’ll learn why this happens and key take-aways to help you avoid BI failure.
Business Intelligence (BI) adoption is exploding, and with good reason. As businesses create more data than ever before, we’re seeing more of these businesses turn to BI to capitalize on that data.
What does BI offer? While I could rattle off a list of benefits, most companies use BI for advantages like easy data access and improved decision-making. They want to give users a quick way to easily access and understand their data.
Yet, despite the advertised benefits, many BI projects still fail. Depending on the survey, I’ve seen BI failure rates range from 50 – 80%. Why? Why do so many BI projects fail?
To help you answer that question, we’ve solicited input from some experts in BI and have compiled their input (as well as some of my own) below. Here are 7 common reasons why BI projects fail, along with a key take-away for each point:
1. Poor understanding of BI
Often presented as a silver bullet, Business Intelligence is surrounded with misconceptions. When a business goes into a BI project with unrealistically high expectations, or without a clear understanding of their needs and goals, the project will likely fail.
“Management rarely has a solid grasp on what they are going to use BI for and how they are going to use it,” says Garret Rempel, a Technology Consultant at MNP. “A BI solution is not magic, it will not fix systemic problems in KPI analysis. To establish an effective BI practice your end users must have a clear vision of the decisions that they want to be able to make based on their business data, how they are going to measure their data to make those decisions, and where that data comes from.”
Do your research! Understand exactly what problems you’re trying to solve, and how the selected BI solution will help you solve those problems.
2. Poor data integrity
Here’s a solid lesson for any type of enterprise application (BI or otherwise): Everything starts with the data. If your data is flawed or isn’t cleaned and validated properly, your BI project will fail. End of story. Not only will it fail, it can have disastrous effects on your company. Imagine the consequences of basing critical business decisions off of flawed data.
“In my experience in over 20 years working with complex financial data the single reason why BI projects fail comes down to data integrity,” says Ian Jackson, Managing Partner at Enshored Inc. “Data that is being repurposed from one area to another in most cases will need cleaning and validating. I’d say that the business architects – product managers in particular – often are too academic about the exercise and fail to understand this. Ensuring a regular human review of all data being used in business engines is important to reduce errors.”
Don’t start a BI project until you have processes in place to ensure data integrity.
3. Lack of executive buy-in
BI projects (like many projects) don’t always go according to plan. Sometimes they require more implementation time, or cost slightly more than expected. When that happens, you need strong support from key executives. Executive buy-in stretches far beyond securing funding for your project. It’s about selling the executives on the benefits of the project and securing a strong commitment.
“Most BI projects are typically expensive to implement and don’t have full executive buy-in,” says Tony Kippen, VP of Analytics at Hipcricket. “When the C-level is both unfamiliar with the benefits and concerned with the costs of a BI project, it’s not likely to receive the company’s full support. Underfunded and unsupported, the proper amount and types of data are left out and the results ignored. Failure then becomes a self-fulfilling prophecy.”
Communicate the benefits to your business executives and secure their support before proceeding on a BI project. Executives must be 100% committed to the project if it has any hope to survive.
4. Incomplete requirements
Just as you need executive buy-in for a successful BI project, understanding end user requirements is equally essential. A BI project will fail if you don’t fully understand who will be using the solution and what they’re trying to accomplish.
“A BI project is likely to fail if the businesses are not interviewed correctly to gather requirements,” says Kippen. “A lack of business understanding, across departments or divisions, leaves the data siloed. Without seeing the full picture, a BI project will never see ROI equal to its potential. Also, if assumptions, rather than researched requirements, are used to implement a BI project, the results will be tainted and not objective. Garbage in results in garbage out and a failed project.”
Before starting your project, understand who will be using the BI solution, the data they require, and the problems they’re trying to solve.
5. Analysis paralysis
Taking the last point one step further, proper requirements gathering also helps you avoid another huge problem: Analysis paralysis. As explained below, trying to use all available data will only make analysis more difficult, and must be avoided.
“Many companies jump head first into BI projects with the mantra ‘if you build it, they will come,’” says Jay Millard, Chief Operating Officer at Amadeus Consulting. “Instead of involving key stakeholders to gather requirements prior to big data migrations, it is common to make the mistake of simply grabbing all data points without validating their relevance to desired outcomes. The paradigm, ‘Let’s just get the data in there, then we’ll figure it out’ is definitely one to avoid. Keep in mind that a good chunk of the effort in a successful BI project comes long before the actual migration occurs. Spend some time answering the question, ‘What decisions do we want to make with this data?’ You may find right out of the gate that the answer to such a question will vary greatly throughout your team. Use disagreement to your advantage; your confidence in the answers to that one question will ultimately determine your success.”
Don’t overwhelm your users with data they don’t need. Understand exactly what data they require, and what they hope to get out of their data.
6. Using BI Tools That No One Likes (or Knows How to Use)
Suppose you’ve done everything correctly so far. You understand BI, you’ve secured executive buy-in, you’ve cleaned your data, and you’ve gathered complete requirements from your users. Even after all of that, the BI tool can kill the whole thing. If users either don’t like the chosen tool, or find it confusing, they likely won’t use it.
“Oftentimes organizations will fall in love with a tool as part of their BI initiative, purchase it, and then decide they do not have budget to adequately train staff,” says Sara Handel, Principal Consultant and BI Lead at Excella Consulting. “Or they will invest in a tool selected by IT professionals, without consulting end-users on their needs or abilities. Organizations using the wrong BI tools bring additional obstacles to project success. Staff members who do not like a specific product will often revert back to the “old way” of doing things – essentially making all your efforts (and money spent!) null and void. In other organizations, only one or two people will truly understand how to use a tool and create bottlenecks to progress, or become bogged down by numerous requests.”
Gathering user input on BI tools before making a purchase, and then providing thorough user training will help ensure user adoption.
7. Inability to pivot
What happens if you’ve gathered all of your requirements for your BI project, only to see your business needs change? Worse yet, what happens if your business needs change after deploying a new solution?
Unfortunately, many BI solutions aren’t built to evolve. They’re built for the business needs that existed during implementation. Maybe the business develops a new product line, switches software platforms, or brings in a new type of database. BI projects or solutions that aren’t built to adapt to these changes will likely fail.
“Most BI projects, even if broken into smaller parts for development, typically don’t have the ability to pivot when the business changes focus or moves a different direction,” explains Kippen. “A new product line, or the discontinuation of an old one, can throw a BI project back to square one. Acquisitions and mergers can also wreak havoc, introducing new systems and data collection methods that must be integrated.”
From your initial planning to your eventual tool selection, plan for change. An inflexible plan or tool can kill an entire project.
So, what do you think? Is there anything you would add to this list? If so, please share your thoughts in the comments.
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